CN101853531B - Helicopter flight state identification method based on presort technology and RBF (Radial Basis Function) neural network - Google Patents

Abstract

The invention discloses a helicopter flight state identification method based on a presort technology and RBF (Radial Basis Function) neural networks, which comprises the following steps of: classifying a flight state to be identified into 10 types and designing an RBF neural network for further identifying the flight state of each type; when identifying the flight state of certain flight date, ranking the flight date into the 10 types according to a certain flight parameters firstly and then inputting the flight data into RBF neural networks corresponding to each type of the flight state to carry out accurate identification of the flight states. The invention reduces the states to be identified by each neural network by the presort technology, thereby enhancing the recognition rate of the helicopter flight states by utilizing the neural networks.

Description

Helicopter flight state identification method based on technology of presorting and RBF neural network

Technical field

The invention belongs to helicopter flight state recognition technical research field, be specifically related to a kind of helicopter flight state identification method based on technology of presorting and RBF neural network.

Background technology

Because there is a large amount of dynamic components in helicopter, so that the helicopter accident rate is about 40 times of fixed wing aircraft, the life prediction of research therefore carry out fault diagnosis and to(for) helicopter just becomes particularly important.And the state of flight that obtains helicopter is one of condition precedent of carrying out helicopter fault diagnosis and life prediction.

Obtain at present the method for state of flight, also mainly rely on manual work to carry out, voice and manual state switching pulse signal through the pilot obtain the in-flight state of flight of helicopter.There is following shortcoming in this method: one, the artificial method that obtains state of flight makes pilot's burden when flight increase, and is unfavorable for flight safety; Two, the pilot is interfered easily and gives the information that makes mistake, and therefore makes the state of flight that is obtained with having deviation between actual state of flight.

Helicopter is when flight, and the multiple sensors of installation can be measured to various flight parameters, and measurement result is saved as corresponding flying quality.Through these flying qualities are handled, the state of flight when obtaining to fly just can avoid the use of the problem that exists when manual method obtains state of flight.

But because the helicopter flight parameter is disturbed and crosslinked coupling effect outside in measurement, inevitably can introducing; Flight parameter usually is again dynamic change in time simultaneously; Even under with a kind of state of flight; Various parameters are also changing, thus the flight parameter of state of flight and each monitoring concern the nonlinear relationship of informal dress from complicacy.Neural network is because of having the good non-linear mapping capability of approaching, and fault-tolerant and extensive ability, in the state recognition research field, is used widely.Flying quality through neural network during to helicopter flight is discerned, and obtains and the corresponding state of flight of flying quality, is a kind of method that obtains the helicopter flight state.But the state of flight of helicopter reaches tens of kinds, and the discrimination of neural network reduces along with the increase of need status recognition, this become use neural network the helicopter flight shape is discerned in a urgent problem.

Summary of the invention

The objective of the invention is: overcome in using the process of nerual network technique the helicopter state recognition; Discrimination is along with the state of flight of need identification increases and the problem of decline; A kind of helicopter flight state identification method based on technology of presorting and RBF (Radial Basis Function, RBF) neural network is provided.

Concrete steps based on the technology of presorting and the helicopter flight state identification method of RBF neural network provided by the invention are following:

Step 1, the state of flight that need discern helicopter are sorted out; Whether the state of flight that helicopter need be discerned turns and two types of non-turnings according to turning to be divided into earlier; The state of flight of non-turn condition is further divided into high-altitude high speed, low altitude high speed and three types of state of flights of low-altitude low-speed; According to velocity range the state of flight of high-altitude high speed and low-altitude low-speed is segmented again; Specifically be that minimum speed, transition speed, speed for maximum endurance, oceangoing voyage speed and maximal rate according to helicopter classified; The state of flight of low-altitude low-speed is subdivided into two types: minimum speed, minimum speed are to transition speed; High-altitude state of flight at a high speed is subdivided into six types: transition speed, speed for maximum endurance, oceangoing voyage speed, maximal rate, and between speed for maximum endurance and oceangoing voyage speed, divide arbitrarily two types; All state of flights that need discern of helicopter are divided into ten groups the most at last;

For some speed change and degree of uprising state, the method for employing is in the middle of several state groups with its branch;

Step 2, be designed for the RBF RBF neural network of the further identification of state of flight for each group of getting in the step 1;

If only comprise a state in certain group, then need be to the RBF neural network of the further identification of this group design;

Step 3, the flying quality that will carry out state of flight identification is handled; At first flying quality is gone wild point, amplitude limit and smoothing processing, use the flying quality of rate of change to carry out match to needs then and try to achieve rate of change, at last flying quality is carried out normalization and handle;

Step 4, will pass through flying quality that step 3 handles and presort in the state of flight of ten groups of being divided in the step 1 according to crab angle interconversion rate, barometer altitude and indicator air speed:

At first according to crab angle rate of change Δ COSI flying quality being divided into and turning and two types of non-turnings, when Δ COSI＞0 is a turn condition during Δ COSI＜0 perhaps, then is non-turn condition when Δ COSI=0;

Next is according to the threshold value kH of barometer altitude Hp and indicator air speed Vi _{P with}k _Vi, the flying quality of non-turn condition is classified with the comparable situation of threshold value according to barometer altitude and indicator air speed, if Hp>=k _HpAnd Vi>=k _ViThen be divided into one type of high speed flight at high altitude state, if Hp＜k _HpAnd Vi>=k _ViThen be classified as one type of low altitude high speed state of flight, if Hp＜k _HpAnd Vi＜k _ViThen be divided into one type of low-altitude low-speed state of flight;

At last, with being divided into the high-altitude at a high speed and the flying quality of low-altitude low-speed state of flight, in the group that further is classified as in the step 1 to be divided according to the scope of indicator air speed Vi;

Step 5, the flying quality that will be classified as each group input in the RBF neural network of each group design, further pick out pairing state of flight.

Helicopter flight state identification method of the present invention, its advantage and good effect are:

(1) flying quality during according to helicopter flight is discerned state of flight through the RBF neural network, is subject to when avoiding the use of manual method disturb, and the problem that discrimination is low has improved the accuracy rate of state of flight identification;

(2) through the technology of presorting, when having solved the use neural network and carrying out the helicopter flight state recognition, the state recognition rate increases the problem that reduces with need status recognition quantity.

Description of drawings

Fig. 1 is the flow chart of steps of helicopter flight state identification method of the present invention;

Fig. 2 is the RBF neural network structure synoptic diagram that the present invention adopts;

Fig. 3 is the synoptic diagram as a result that the embodiment of the invention adopts the inventive method to test to three groups of flying qualities;

Fig. 4 is the flight envelope of helicopter.

Embodiment

To combine accompanying drawing and embodiment that the present invention is done further detailed description below.

Flying quality with certain model helicopter is an example, uses the helicopter flight state identification method based on technology of presorting and RBF neural network provided by the present invention that its state of flight is discerned, and as shown in Figure 1, concrete steps are following:

Step 1,35 kinds of state of flights of need identification are sorted out;

State of flight when table 1 is depicted as this model helicopter flight has 42 kinds.Wherein needing the state of flight of identification is 35 kinds, and 4 in the table 1,7,8,9,10,11 and 32 these 7 states do not need identification.

The state of flight of table 1 helicopter

Whether these 35 states turned according to helicopter is divided into two big types, both turn condition and non-turn conditions.In non-turn condition, can be divided into three major types according to height/low latitude, high/low speed, promptly the high-altitude at a high speed, low altitude high speed and low-altitude low-speed three major types, this state of high-altitude low speed does not exist.The flight envelope of helicopter as shown in Figure 4; The barometer altitude scope of helicopter is about 100～600 meters; Flying speed 0～230 km/hour about, so the threshold value that will distinguish the high low latitude of helicopter among the present invention is decided to be 270 meters, the threshold value of high low speed be decided to be 75 kms/hour.Wherein, the high-altitude refers to the barometer altitude of helicopter more than or equal to 270 meters, and the low latitude refers to the barometer altitude of helicopter less than 270 meters, the indicator air speed that low speed refers to helicopter less than 75 kms/hour, the indicator air speed that refers to helicopter at a high speed more than or equal to 75 kms/hour.In above three major types state, segment again according to velocity amplitude, like table 2; Wherein the division of classification foundation medium velocity scope is according to this type helicopter minimum speed (＜4km/h km/hour), and minimum speed to transition speed (4～74km/h), transition speed (75～94km/h); Speed for maximum endurance (94～130km/h); Oceangoing voyage speed (190～215km/h), for ease of the refinement of state of flight, it is being divided into two types of (130～170km/h and 170～190km/h) between speed for maximum endurance and the oceangoing voyage speed; And maximal rate (being peak power), 35 kinds of state of flights are divided into 0～90 group the most at last.To the helicopter of different model,, can classify according to minimum speed, transition speed, speed for maximum endurance, oceangoing voyage speed and the maximal rate of this type helicopter according to the method described above equally though its velocity range is different.Wherein, this of low altitude high speed type state of flight that comprises has only a kind of, so this group does not need to segment according to flying speed again.

The explanation of table 2 sorting technique

Need to prove that in order to improve the accuracy of classification as far as possible, for some speed change and degree of uprising state, the method for employing is in the middle of several state groups with its branch.As in the table 2, for example the state of flight code name of helicopter is 30 horizontal deceleration regime because speed change is divided in state of flight the 2nd, 3,4,5 groups.

Step 2, be the RBF neural network that 0～9 each group is designed for the further identification of state of flight:

In step 1, all state of flights have been divided into 10 groups, have been numbered 0 to 9, wherein 7 of groups contain a state, so do not need to discern, other 9 groups need use the RBF neural network that state of flight is further discerned again.

RBF (Radial Basis Function, RBF) neural network is made up of input layer, hidden layer and output layer, wherein is made up of the Gaussian function mapping between input layer and the hidden layer, and output layer and hidden layer are made up of the linear function mapping.Its structure is shown in accompanying drawing 2.X wherein ₁～x _nBe the input vector of input layer, dimension is n, h ₁～h _mBe hidden layer node, the node number is m; y ₁～y _kBe output layer vector dimension, dimension is k, and wherein, n, m, k are the integer greater than 0.

The available parameter of state of flight identification is as shown in table 3,1～23 flight parameter that writes down during for helicopter flight wherein, and 24～26 is the rate of change of indicator air speed, radio altitude and crab angle in the flight parameter.

The parameter list that the identification of table 3 state of flight is available

In the embodiment of the invention, need further to be designed for again the RBF neural network of each group state of flight identification.Input layer vector dimension n, the hidden layer node that is used for the RBF neural network of each group state of flight identification counted the selection of m and output layer vector dimension k and seen table 4.Wherein status categories is the group numbering in the corresponding table 2; Input layer vector dimension n equals to be used for the further kind of the neural network input vector of identification of this group state of flight; Also be to be used to discern the needed flight parameter number of this group state of flight in the table 3; With status categories 0 is example; Be used for this group state of flight further the input layer vector dimension of the RBF neural network of identification be 3, and the kind of input layer vector is 1,9 and 25, this code name is corresponding with the available parameter code name of table 3 state of flight identification.Output layer vector dimension k equates with the pairing state of flight number of this group, shown in status categories one row of table 2.Hidden layer node number at first rule of thumb formula is chosen, and experimental formula is shown in (11) formula:

$m=\sqrt{n+k}+d---\left(11\right)$

Wherein: m is the hidden layer node number, and n is an input layer vector dimension, and k is an output layer vector dimension, and d is the constant between 1～10.Can make amendment to the hidden layer node number according to neural network hands-on situation,, can reduce the hidden layer node number,, can suitably increase the number of hidden layer node if the neural network precision does not reach requirement if the neural network convergence slowly.

Table 4 is used for the RBF neural network parameter tabulation of all kinds of state of flight identifications

The output y of RBF neural network _k, as shown in Figure 2, shown in (12) formula:

$y_k=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{w}_{\mathrm{ik}}{h}_i---\left(12\right)$

W in the formula ₁₁～w _1kW _M1～w _MkBe neural network weight; h ₁～h _mBe the RBF of neural network, choose Gaussian function here, the expression formula of Gaussian function is shown in (13) formula:

$h_i\left(\stackrel{\→}{x}\right)=\exp [-\frac{{\left|\right|\stackrel{\→}{x}-c_i\left|\right|}^2}{2{\mathrm{\σ}}_i^2}],$ i＝1，2，......m (13)

In the formula Be the input vector of n dimension, c _iBe the center of i RBF, be with

Vector with same dimension, σ _iBe i perceptron variable, || || expression Euler norm, m, n, k are the integer greater than 0.

To RBF neural network structure parameter c in (13) formula _iAnd σ _iUse the K mean algorithm to confirm, come the RBF neural network is trained, confirm neural network weight w in (12) formula through the gradient descent method ₁₁～w _1kW _M1～w _Mk, and then accomplish and to be used for the further design of the RBF neural network of identification of each group state of flight, being numbered one type of state of flight of 0 with group in the table 2 below is that example is explained.

Group is numbered a type of one type of state of flight correspondence table 4 status categories 0 of 0 in the table 2; Such state of flight that comprises have 15 (pull-up turn or the risings of spiraling), 35 (with the speed for maximum endurance flat bank or spiral), 36 (with h the velocity level turn), 37 (turning) with maximum cruise, these several kinds of state of flights are encoded once more, for example; State 15 is 1; State 35 is 2, and state 36 is 3, and state 37 is 4.The output layer vector dimension of RBF neural network is identical with the state of flight number that this group comprised, and for status categories 0, the RBF neural network output layer vector dimension that is used for the further identification of its state of flight is 4.When the RBF neural network of this group is trained, make the corresponding a kind of state of flight of each dimension output vector of output layer, for example use y ₁Represent state of flight 15, this neural network is trained through formula (12).

Work as trained; When one group of flying quality is judged; Each dimensional vector of comparative neural network output layer can be provided with a threshold value with the similarity degree of encoded radio, judges that output vector is whether in the scope of this threshold value; If the output vector and the encoded radio of certain one dimension meet, then recognition result is the pairing state of flight of output vector.

Step 3: the flying quality to carrying out state of flight identification is handled:

Because the sensor that carries on the helicopter is numerous, work under bad environment, the interference that receives is many, so flying quality is carried out need handling these flying qualities before the state of flight identification, the method for processing is following:

(1) flying quality is gone wild point, amplitude limit and smoothing processing:

Wild point refer to the value of certain sampled point in the flying quality and before and after it variable gradient between value of a sampled point be that helicopter can not reach in the next sampling period in real flight conditions, the reason that produces wild point is loss of data or serious disturbance.If a flying quality p is p (t) in the value of sampling instant t in one group of flying quality of flight parameter, the value of previous sampling instant is p (t-1), and the variable gradient Δ p that tries to achieve in this sampling period is suc as formula (14):

Δp＝|p(t)-p(t-1)| (14)

The greatest gradient value Δ p that this Grad can be reached in a sampling period with this flight parameter _MaxCompare, if Δ p>=Δ p _Max, think that then p (t) is wild point, with its rejecting;

Amplitude limit is that the point that does not meet the helicopter real flight conditions in the flying quality is rejected; Establish equally that a flying quality p is p (t) in the value of sampling instant t in one group of flying quality of flight parameter, with its maximal value p that can reach when the helicopter practical flight with this flight parameter _MaxAnd minimum value p _MinDo comparison, if p (t)＞p _MaxOr p (t)＜p _Min, then the value of t this flight parameter of the moment is rejected from flying quality;

Smoothly be flying quality to be carried out filtering with filtering technique, elimination in the flying quality gatherer process, the noise signal that is interfered and produces owing to sensor.The filtering method that flying quality is used is average rate wave method: establish that a flying quality p is p (t) in the value of sampling instant t in one group of flying quality of flight parameter, get each r point before and after it, the value that makes p order equals the mean value of this 2r+1 the value of putting, that is:

$p\left(t\right)=\underset{i=t-r}{\overset{t+r}{\mathrm{\Σ}}}p\left(i\right)/2r+1---\left(15\right)$

(2) use the flying quality of rate of change to carry out match to needs, try to achieve rate of change:

The flight parameter that is used for discerning state of flight comprises speed (referring to indicator air speed in the table 3), highly (refers to barometer altitude in the table 3) and the rate of change of crab angle, therefore, need carry out match to speed, height and crab angle data and obtain corresponding rate of change.

To the every bit in the flying quality of above-mentioned three kinds of flight parameters, get before it and each n point afterwards, carry out least square fitting, promptly carry out the match of straight line with least square method, the slope of straight line be the rate of change that will obtain.

If j is the number of participating in the point of match, be j=2r+1, r is a natural number.If the functional form of straight line is y=a+bt.The data that experiment records are (t ₁, y ₁), (t ₂, y ₂) ..., (t _j, y _j):, to t ₁, t ₂..., t _j, the optimum value of y (regressand value) is a+bt ₁, a+bt ₂..., a+bt _jWith the principle of least square a that derives, the value of b should satisfy the measured value y of y _iWith regressand value a+bt _iThe quadratic sum of difference get minimum, that is:

$\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{[y_i-(a+{\mathrm{bt}}_i)]}^2=\min ---\left(16\right)$

Select a, b makes the necessary condition of formula (16) minimalization be:

$\left\{\begin{array}{c}\frac{\∂}{\∂a}\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{[y_i-(a+{\mathrm{bt}}_i)]}^2=0\\ \frac{\∂}{\∂b}\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{[y_i-(a+{\mathrm{bt}}_i)]}^2=0\end{array}\right.---\left(17\right)$

Further deriving has:

$\left\{\begin{array}{c}\underset{i=1}{\overset{j}{\mathrm{\Σ}}}2[y_i-(a+{\mathrm{bt}}_i)](-1)=0\\ \underset{i=1}{\overset{j}{\mathrm{\Σ}}}2[y_i-(a+{\mathrm{bt}}_i)](-t_i)=0\end{array}\right.---\left(18\right)$

Can obtain after the arrangement:

$\left\{\begin{array}{c}\mathrm{ak}+b\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{t}_i=\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{y}_i\\ a\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{t}_i+b\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{t}_i^2=\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{t}_i{y}_i\end{array}\right.---\left(19\right)$

Therefrom can solve:

$\left\{\begin{array}{c}b=\frac{\mathrm{\Σ}{t}_i\mathrm{\Σ}{y}_i-\mathrm{j\Σ}{t}_i{y}_i}{{\left(\mathrm{\Σ}{t}_i\right)}^2-\mathrm{j\Σ}{t}_i^2}\\ a=\frac{\mathrm{\Σ}{t}_i{y}_i\mathrm{\Σ}{t}_i-\mathrm{\Σ}{y}_i{t}_i^2}{{\left(\mathrm{\Σ}{t}_i\right)}^2-\mathrm{j\Σ}{t}_i^2}\end{array}\right.---\left(20\right)$

In the formula, a, b is called regression coefficient, and t is the time, and y is the numerical value of speed (or height, crab angle), and the b value of the each point that obtains after the match is the rate of change of this point.

(3) flying quality being carried out normalization handles:

Certain neuronic input summation is as the input of excitation function in the neural network, and the result of excitation function computing is as this neuronic output.Excitation function is selected the Sigmoid function for use, and the characteristics of Sigmoid function are the responses that input stimulus is produced a localization.Variation away from 0 function of region value is very smooth, promptly only drops near the zero very little appointed area when input, and the Sigmoid function is just made meaningful response, and response is between 0 to 1.And excessive or too small when input value, neuron will be near saturated so.Flight parameter as state of flight identification has more than 20, and each parameter dimension is different, size is totally different, so using these parameters before this, is necessary that flying quality to each flight parameter carries out normalization and handles.The normalization formula adopts following formula:

$y=\frac{x-\frac{1}{2}(x_{\max }+x_{\min })}{\frac{1}{2}(x_{\max }-x_{\min })}---\left(21\right)$

In the formula: y is that back flying quality value is handled in normalization; X is a certain flying quality value before handling; x _MaxMaximal value for the corresponding flying quality of this flight parameter; x _MinBe its minimum value.Handle through normalization, the codomain of each flight parameter is transformed into the scope of [1,1].

Step 4: will pass through flying quality that first three step handles according to the crab angle interconversion rate, barometer altitude and the indicator air speed first step of presorting is divided in the state of flight of 0～90 group:

According to the rate of change Δ COSI of crab angle flying quality is divided into and turns and two types of non-turnings; When Δ COSI＞(perhaps Δ COSI＜(time be turn condition; Then be non-turn condition when Δ COSI=0, the flying quality that is divided into turn condition is classified as the 0th type in the table 2.

If the threshold value of barometer altitude Hp and indicator air speed Vi is respectively k _HpAnd k _Vi, get k _HpBe 270 meters, k _ViBe 75 kms/hour; The flying quality that is divided into non-turn condition according to barometer altitude and the indicator air speed comparable situation with threshold value, is further divided into three types of high-altitude high speeds, low altitude high speed, low-altitude low-speed, if Hp>=k _HpAnd Vi>=k _ViThen be divided into one type of high speed flight at high altitude state, if Hp＜k _HpAnd Vi>=k _ViThen be classified as one type of low altitude high speed state of flight, if Hp＜k _HpAnd Vi＜k _ViThen be divided into one type of low-altitude low-speed state of flight.

The flying quality that is divided into the high-altitude fast state, basis is always apart from displacement w earlier _f, being classified as the 1st type in the table 2 with always reaching maximum flying quality apart from displacement, remaining flying quality is classified as the 2nd～6 type in the table 2 according to the scope of indicator air speed Vi again; The flying quality that is divided into the low altitude high speed state is classified as the 7th type in the table 2; The flying quality that is divided into the low-altitude low-speed state is classified as the 8th～9 type in the table 2 according to the scope of indicator air speed.

Just the flying quality that needs state of flight identification is presorted through above-mentioned division.The threshold value k of crab angle rate of change wherein _{Δ COSI}And the threshold value k of barometer altitude and indicator air speed _ViAnd k _HpChoose according to the model of concrete helicopter and confirm.

Step 5: the flying quality that will be classified as each group is input as in the RBF neural network of each group design, further picks out pairing state of flight.

In the embodiment of the invention; The encoded radio of establishing three state of flights in the test is: flat (0 ° of the yaw angle) coding 0 that flies of oceangoing voyage speed; With flat (10 ° of the left yaw angles) coding 1 that flies of oceangoing voyage speed; With flat (10 ° of the right yaw angles) coding-1 that flies of oceangoing voyage speed, when the difference of output valve and encoded radio less than 0.5 the time, think that recognition result is correct.As shown in Figure 3; 1) oceangoing voyage speed is flat to fly in the synoptic diagram as a result of (0 ° of yaw angle); The dbjective state encoded radio is 0; The flying quality of 6000 sampled point gained is used state identification method of the present invention, and the definite state of flight of its output state encoded radio between threshold value 0.5 and-0.5 flies (0 ° of yaw angle) for oceangoing voyage speed is flat, and what exceed threshold value can not be identified as this state of flight.Equally, 2) oceangoing voyage speed is flat flies in the synoptic diagram of (10 ° of left yaw angles), and the dbjective state encoded radio is 1, gets 7000 sampled points, and the threshold value of output state encoded radio is 0.5 and 1.5; 3) oceangoing voyage speed is flat to fly in the synoptic diagram of (10 ° of right yaw angles), and the dbjective state encoded radio is-1, gets 5000 sampled points, and the threshold value of output state encoded radio is-0.5 and-1.5.Finally, the recognition result of three state of flights is respectively 97.5%, 100% and 96.1% through statistical correction rate, and the average recognition correct rate of state of flight is 98.1%, and this discrimination reaches the requirement of flying quality state classification.

Claims (7)

1. the helicopter flight state identification method based on technology of presorting and RBF neural network is characterized in that, may further comprise the steps:

Step 1, the state of flight that need discern helicopter are sorted out; Whether the state of flight that helicopter need be discerned turns and two types of non-turnings according to turning to be divided into earlier; The state of flight of non-turn condition is further divided into high-altitude high speed, low altitude high speed and three types of state of flights of low-altitude low-speed; According to velocity range the state of flight of high-altitude high speed and low-altitude low-speed is segmented again; Specifically be that minimum speed, transition speed, speed for maximum endurance, oceangoing voyage speed and maximal rate according to helicopter classified; The state of flight of low-altitude low-speed is subdivided into two types: minimum speed, minimum speed are to transition speed; High-altitude state of flight at a high speed is subdivided into six types: transition speed, speed for maximum endurance, oceangoing voyage speed, maximal rate, and between speed for maximum endurance and oceangoing voyage speed, divide arbitrarily two types; All state of flights that need discern of helicopter are divided into ten groups the most at last;

For some speed change and degree of uprising state, the method for employing is in the middle of several state groups with its branch;

Said low latitude refers to the barometer altitude of helicopter less than 270 meters, and the high-altitude refers to that the barometer altitude of helicopter is more than or equal to 270 meters; The indicator air speed that low speed refers to helicopter less than 75 kms/hour, the indicator air speed that refers to helicopter at a high speed more than or equal to 75 kms/hour;

Step 2, be designed for the RBF RBF neural network of the further identification of state of flight for each group of getting in the step 1;

If only comprise a state in certain group, then need be to the RBF neural network of the further identification of this group design;

Step 3, the flying quality that will carry out state of flight identification is handled; At first flying quality is gone wild point, amplitude limit and smoothing processing, use the flying quality of rate of change to carry out match to needs then and try to achieve rate of change, at last flying quality is carried out normalization and handle;

Step 4, will pass through flying quality that step 3 handles and presort in the state of flight of ten groups of being divided in the step 1 according to crab angle interconversion rate, barometer altitude and indicator air speed:

At first according to crab angle rate of change Δ COSI flying quality being divided into and turning and two types of non-turnings, when Δ COSI＞0 is a turn condition during Δ COSI＜0 perhaps, then is non-turn condition when Δ COSI=0;

Next is according to the threshold value k of barometer altitude Hp and indicator air speed Vi _HpAnd k _Vi, the flying quality of non-turn condition is classified with the comparable situation of threshold value according to barometer altitude and indicator air speed, if Hp>=k _HpAnd Vi>=k _ViThen be divided into one type of high speed flight at high altitude state, if Hp＜k _HpAnd Vi>=k _ViThen be classified as one type of low altitude high speed state of flight, if Hp＜k _HpAnd Vi＜k _ViThen be divided into one type of low-altitude low-speed state of flight;

At last, with being divided into the high-altitude at a high speed and the flying quality of low-altitude low-speed state of flight, in the group that further is classified as in the step 1 to be divided according to the scope of indicator air speed Vi;

Step 5, the flying quality that will be classified as each group input in the RBF neural network of each group design, further pick out pairing state of flight.

2. helicopter flight state identification method according to claim 1; It is characterized in that; The RBF RBF neural network that is designed for the further identification of state of flight described in the step 2, the RBF neural network is made up of input layer, hidden layer and output layer;

Input layer vector dimension n equals to be used to discern the number of the needed flight parameter of this group state of flight;

Output layer vector dimension k equals the pairing state of flight number of this group;

Hidden layer node count m rule of thumb formula choose; Said experimental formula is:

wherein, d is the constant between 1～10;

For the further flight group that needs identification, comprise more than one state of flight in this flight group, according to the output formula of following RBF neural network, each dimension output vector of training RBF neural network output layer is to a kind of state of flight in the group of should flying;

Wherein, the output y of RBF neural network _kFor:

$y_k=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{w}_{\mathrm{ik}}{h}_i$

Wherein, w ₁₁～w _1kW _M1～w _MkBe neural network weight; h ₁～h _mBe the RBF of neural network, choose Gaussian function here, its expression formula is:

$h_i\left(\stackrel{\→}{x}\right)=\exp [-\frac{{\left|\right|\stackrel{\→}{x}-c_i\left|\right|}^2}{2{\mathrm{\σ}}_i^2}],$ i＝1，2，......m

Wherein,

Be the input vector of n dimension, c _iBe the center of i RBF, be with

Vector with same dimension, σ _iBe i perceptron variable, || || expression Euler norm; N, m, k are the integer greater than 0;

To RBF neural network structure parameter c _iAnd σ _iUse the K mean algorithm to confirm, come the RBF neural network is trained, confirm the weight w of neural network through the gradient descent method ₁₁～w _1kW _M1～w _Mk

3. helicopter flight state identification method according to claim 1 is characterized in that, described in the step 3 flying quality is gone wild point, amplitude limit and smoothing processing, is specially:

The first step, remove wild point; If a flying quality p is p (t) in the value of sampling instant t, the value of previous sampling instant is p (t-1), and then the variable gradient Δ p in this sampling period is: Δ p=|p (t)-p (t-1) |;

The greatest gradient value Δ p that this variable gradient Δ p can be reached in a sampling period with this flying quality _MaxCompare, if Δ p>=Δ p _Max, think that then p (t) is wild point, with its rejecting;

Second step, amplitude limit; If a flying quality p is p (t) in the value of sampling instant t, with its maximal value p that can reach when the helicopter practical flight with this flying quality _MaxAnd minimum value p _MinDo comparison, if p (t)＞p _MaxOr p (t)＜p _Min, then p (t) is rejected from flying quality;

The 3rd step, level and smooth; Adopt filtering technique that flying quality is carried out filtering, the filtering method that uses is average rate wave method:

If a flying quality p is p (t) in the value of sampling instant t, get its each r point in front and back, the value that makes p order equals the mean value of the value of this 2r+1 point:

$p\left(t\right)=\underset{i=t-r}{\overset{t+r}{\mathrm{\Σ}}}p\left(i\right)/2r+1$

Wherein, r is a natural number.

4. helicopter flight state identification method according to claim 1 is characterized in that, the flying quality to needs use rate of change described in the step 3 carries out match, tries to achieve rate of change, is specially:

Flying quality to indication speed, barometer altitude and three flight parameters of crab angle carries out match, obtains percentage speed variation, altitude rate and crab angle rate of change;

To each flying quality of indication speed, barometer altitude and crab angle flight parameter, get before it and each n flying quality afterwards, carry out the match of straight line with least square method, the slope of straight line be the rate of change that will obtain;

If the functional form of straight line is y=a+bt, the data that experiment records are (t ₁, y ₁), (t ₂, y ₂) ..., (t _j, y _j), to t ₁, t ₂..., t _j, the regressand value of y is a+bt ₁, a+bt ₂..., a+bt _j, j is the number of participating in the point of match, j=2r+1, r are natural number; With the principle of least square a that derives, the value of b should satisfy the measured value y of y _iWith regressand value a+bt _iThe quadratic sum of difference get minimum:

$\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{[y_i-(a+{\mathrm{bt}}_i)]}^2=\min$

Select a, b makes the necessary condition of minimalization be:

$\left\{\begin{array}{c}\frac{\∂}{\∂a}\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{[y_i-(a+{\mathrm{bt}}_i)]}^2=0\\ \frac{\∂}{\∂b}\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{[y_i-(a+{\mathrm{bt}}_i)]}^2=0\end{array}\right.$

Therefrom solve:

$\left\{\begin{array}{c}b=\frac{\mathrm{\Σ}{t}_i\mathrm{\Σ}{y}_i-\mathrm{j\Σ}{t}_i{y}_i}{{\left(\mathrm{\Σ}{t}_i\right)}^2-\mathrm{j\Σ}{t}_i^2}\\ a=\frac{\mathrm{\Σ}{t}_i{y}_i\mathrm{\Σ}{t}_i-\mathrm{\Σ}{y}_i{t}_i^2}{{\left(\mathrm{\Σ}{t}_i\right)}^2-\mathrm{j\Σ}{t}_i^2}\end{array}\right.$

Wherein, a, b is a regression coefficient, and t represents the time, and y is the numerical value of indication speed or barometer altitude or crab angle, and the b value of the each point that obtains after the match is the rate of change of this flying quality.

5. helicopter flight state identification method according to claim 1 is characterized in that, flying quality is carried out normalization handle described in the step 3 is specially:

Excitation function is selected the Sigmoid function for use in the neural network, and Sigmoid function response is carried out normalization to flying quality and handled between 0 to 1, adopts the normalization formula:

$y=\frac{x-\frac{1}{2}(x_{\max }+x_{\min })}{\frac{1}{2}(x_{\max }-x_{\min })}$

Wherein, y is that back flying quality value is handled in normalization, and x is a certain flying quality value before handling, x _MaxBe the maximal value of this flying quality, x _MinMinimum value for this flying quality;

Handle through normalization, the codomain of each flying quality is transformed into the scope of [1,1].

6. helicopter flight state identification method according to claim 1; It is characterized in that; Step 4 is said will to be divided into the high-altitude at a high speed and in the flying quality of the low-altitude low-speed state of flight group that further is classified as in the step 1 to be divided according to the scope of indicator air speed Vi; Specifically: in the flying quality of high speed flight at high altitude state, will be always apart from displacement w _fReach in one type of the maximal rate under the high speed flight at high altitude state that maximum flying quality is classified as in the step 1 to be divided; Remaining flying quality of high speed flight at high altitude state further is classified as transition speed, speed for maximum endurance, oceangoing voyage speed according to the scope of indicator air speed Vi again, and between speed for maximum endurance and oceangoing voyage speed, divide arbitrarily two types these five types in; And, be classified as in one type of one type of minimum speed or minimum speed to the transition speed according to the scope of indicator air speed with the flying quality of low-altitude low-speed state.

7. helicopter flight state identification method according to claim 1 is characterized in that, the threshold value k of the Hp of barometer altitude described in the step 4 _HpBe 270 meters, the threshold value k of indicator air speed Vi _ViBe 75 kms/hour.

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